Papers

Radiative-conductive systems are intrinsically nonlinear due to the quartic temperature dependence of thermal radiation. Under fixed total heating power, convexity arguments imply that nonuniform temperature distributions radiate more efficiently and therefore exhibit a lower mean temperature than their isothermal counterparts. However, this conclusion remains qualitative, and an explicit quantitative relation between temperature heterogeneity and mean temperature reduction has been lacking. Here we derive a variance-based analytical expression linking the area-averaged temperature to the corresponding isothermal equilibrium temperature in a nonuniformly heated radiative–conductive system. By integrating the governing equation and performing a systematic second-order expansion about the ambient temperature, we show that the decrease of the mean temperature relative to the isothermal equilibrium value is linearly proportional to the temperature variance, with a proportionality coefficient set solely by the ambient temperature. This result transforms the convexity-based inequality into a quantitative statistical relation within the perturbative regime and provides a physically transparent framework for describing nonlinear radiative averaging in thermally heterogeneous systems.

Effects of neoclassical toroidal viscosity (NTV) on plasma flow evolution in the presence of resonant magnetic perturbation (RMP) in a tokamak have been evaluated using a cylindrical theory model. Calculations show that the introduction of NTV has almost no effect on the flow on the resonant surface, so the locked or unlocked state on the resonant surface remains unchanged, but it impacts the rotation profile in the core region. The toroidal, poloidal, and parallel flows in the core region are slightly reduced with uniform pressure. For non-uniform pressure profiles, elevated $\beta$ enhances the global amplitude of NTV torque but suppresses that of electromagnetic (EM) torque. These two driving terms collectively maintain the locked mode state.

We propose an SIS competition model describing the propagation of conflicting rumors, such as fake news and its corrections. This simple model captures the interaction between rumor propagation and opinion dynamics, where rumors drive opinion changes and, conversely, individuals' opinions determine the infection rates of rumors. We analytically derive all steady states and their stability. These results uncover a novel coexistence mechanism. This coexistence corresponds to a scenario where belief in one rumor (e.g., fake news) paradoxically aids the spread of the opposing rumor (e.g., corrective information). Due to this mechanism, a nontrivial but realistic phenomenon occurs where a lower infection rate actually enhances the spread of a rumor. Furthermore, although the model does not explicitly incorporate majority conformity, a phenomenon where the majority gains an advantage emerges spontaneously. Consequently, even if one rumor has a higher infection rate, it may be eliminated by the other if its initial share fails to exceed a critical threshold. We analytically derive this threshold using the singular perturbation method.

State-of-the-art ensemble Kalman filtering (EnKF) algorithms require incorporating localization techniques to cope with the rank deficiency and the inherited spurious correlations in their error covariance matrices. Localization techniques are mostly ad-hoc, based on some distances between the state and observation variables, requiring demanding manual tuning. This work introduces a new ensemble filtering approach, which is inherently localized, avoiding the need for any auxiliary localization technique. Instead of explicitly applying localization on ensembles, the idea is to first localize the continuous analysis probability density function (pdf) before ensemble sampling. The localization of the analysis pdf is performed through an approximation by a product of independent marginal pdfs corresponding to small partitions of the state vector, using the variational Bayesian optimization. These marginals are then sampled following stochastic EnKF and deterministic ensemble transform Kalman filtering (ETKF) procedures, using ensembles larger than the partitions' size. The resulting filters involve the same forecast steps as their standard EnKF and ETKF counterparts but different analysis steps, iteratively adjusting the EnKF and ETKF updates of each partition based on the ensemble means of the other partitions. Numerical experiments are conducted with the Lorenz-96 model under different scenarios to demonstrate the potential of the proposed filters. The new filters' performances are comparable to those of the EnKF and ETKF with already tuned localization, both in terms of computational burden and estimation accuracy.

Despite advancements in surgical techniques, hernia recurrence rates remain high, underscoring the need for improved understanding of abdominal wall behaviour. While surgeons are aware of many factors contributing to hernia occurrence (e.g obesity, smoking, surgical technique or site infection), it would be of interest to consider it as a biomechanical pathology. Indeed, an abdominal hernia arises from an imbalance between abdominal wall deformability and applied forces. This review article discusses how biomechanics offer a quantitative framework for assessing healthy and damaged tissue behaviour, guiding personalised surgical strategies throughout the pre-, intra-, and post-operative periods. The abdominal wall is a dynamic, load-bearing structure, continuously subjected to intra-abdominal pressure and mechanical stress. Its biomechanical properties, including elasticity and resistance to loading forces, dictate its function and response to surgical intervention. The linea alba is the stiffest component experiencing the highest stress, while the abdominal wall's anisotropic nature influences deformation patterns. Various experimental and computational methods enable biomechanical characterisation. Hernias represent mechanical failures at anatomical weak points. While surgeons qualitatively evaluate abdominal wall's biomechanics by estimating deformation and closure forces, functional imaging (elastography, dynamic acquisitions) could provide objective biomechanical insights. Hernia formation alters abdominal wall biomechanics, inducing greater mobility and elasticity. Surgical repair fundamentally alters the biomechanics of the abdominal wall. The choice of defect's suturing technique, mesh properties, placement, overlap and fixation methods (e.g. suture, tacks) significantly influence mechanical outcomes. Surgical repair tends to restore physiological biomechanics by re-establishing force transmission and hernia-induced excessive mobility. Suturing techniques, mesh selection and placement influence mechanical outcomes. However, optimal results require implants with mechanical properties mimicking native tissue. Lightweight meshes (<70 g/m2) placed in a retrorectus position, combined with a small-bite suture technique, have been associated with lower recurrence rates and improved post-operative function. By bridging biomechanics with surgical practice, this review highlights how mechanical principles shape hernia formation, diagnosis, and repair. A deeper integration of biomechanical principles into surgical decision-making could refine hernia management and lead to patient-specific, mechanics-informed strategies. For surgeons, this knowledge is not just academic - it is practical and can make a difference to patient outcomes.

Objective: Real-time adaptive proton range verification systems based on produced neutrons require accurate information on their non-isotropic momentum distributions within short times, for which Monte Carlo (MC) methods are too computationally expensive. We present a surrogate model based on Fourier Neural Operators (FNO) for fast prediction of angle- and energy-resolved proton transport and neutron production within proton therapy. Approach: We treat the irradiated phantom and the proton beam's state as depth-evolving series, respectively of different materials, and of spatial, angular and energy phase space density distributions. The task is solved auto-regressively by learning changes in the distributions of protons and those of produced neutrons. For training and evaluation, two datasets of 47 MC simulations featuring different primary intensities were produced. Simulated geometries were extracted from a thoracic CT scan as series of laterally homogeneous materials. Main Results: An average relative $L^2$ discrepancy of 0.067 and 0.137 was achieved by the predicted proton and neutron distributions, respectively. This corresponded to an average gamma passing rate in the spatial distributions of 99.95$\%$ and 99.40$\%$. Training with higher primary intensities led to improvements between 12$\%$ and 30$\%$ in density metrics. Inference over depths of 40 cm at a resolution of 0.5 mm required on average 23.17 s per beam. Significance: The proposed proton beam surrogate generates accurate spatial and momentum distributions of neutrons at MC-level accuracy within seconds, while demonstrating robust generalization with respect to irradiated geometry and beam characteristics. This approach can be used for prototyping and operation of range verification systems, other tasks such as neutron dose estimation, and can be extended to include other kinds of secondary emissions.

We present experimental study of nonradiative double electron capture processes in collisions of 95 and 146 MeV/u bare xenon ions with krypton and xenon gaseous atoms at the HIRFL-CSR storage ring. Angular distributions of the characteristic K{\alpha} radiation of the down-charged projectile ions Xe52+* are measured, which are closely related to the magnetic sublevel population of the excited 1s2l_j states of Xe52+*. It was found that the K{\alpha}1 radiation shows pronounced anisotropic and is sensitive to the collision energies and the target atoms, whereas the K{\alpha}2 radiation gives rise to isotropic. Moreover, obviously difference in the anisotropy parameters of Lyman-{\alpha}1 of Xe53+* ions and K{\alpha} transitions of Xe52+* ions separately following nonradiative single and double electron capture into the L-shell levels of projectiles is obtained and discussed.

The integrity of plasma-facing components (PFCs) in tokamaks is critically challenged by transient events such as runaway electron (RE) impacts. We report the first systematic analysis of the thermal damage to tungsten-based PFC tiles comprising the SPARC outboard off-midplane limiters that is induced by RE beams formed during vertical displacement events. Parametric scans in RE impacting characteristics as well as energy-pitch distribution functions from the Dream code are employed for calculations of the volumetric heat loads. A realistic panel design is adopted to enhance the fidelity of the thermal analysis. The PFC thermal responses are compared in terms of in-depth temperature profiles and damage characteristics, such as melt depth and vaporization losses.

We present a time-domain scheme for computing Casimir forces within the Maxwell stress tensor formalism, together with a specific realization using the finite-element-based discontinuous Galerkin time-domain method. The approach enables accurate evaluation of Casimir–Lifshitz interactions for a wide range of geometries and material properties at finite temperature. At the core of the method, the electromagnetic Green's tensor is expressed as the system's response to dipolar excitations, thereby recasting the Maxwell stress tensor into a set of classical scattering problems driven by electric and magnetic dipoles. We validate the approach against reference calculations of the Casimir interaction between parallel half-spaces at both zero and nonzero temperature. We further demonstrate its applicability to finite, cylindrically symmetric geometries for which closed-form solutions are unavailable, obtaining accurate agreement with asymptotic predictions based on physical considerations. These findings illustrate the method's potential for studying Casimir interactions in realistic micro- and nanoscale structures, relevant to nanodevice design and experimental settings.

We present a generative framework for inverse design of five-layer transmissive Huygens' metasurfaces (HMSs), addressing a longstanding challenge in achieving full-phase, high-efficiency unit cell designs with minimal full-wave simulations. The key to achieving this is our reliance on the field-based semianalytical (SA) scheme developed in Part I of this paper, which allows rapid and highly effective synthesis of such multilayer composites, however with limited accuracy. To overcome the prohibitive data demands of traditional pipelines, we employ Mamba, a selective state space model well suited for long-range sequence modeling as the backbone of our learning framework. A bidirectional Mamba (Bi-Mamba) forward surrogate is first trained on SA-generated data and subsequently fine-tuned with full-wave CST samples. An ablation over a 1080-sample CST pool shows that as few as 270 full-wave calibration samples suffice to reach near-CST-level agreement at a fraction of the simulation cost. An autoregressive Mamba inverse generator is subsequently trained on surrogate-augmented data, treating unit-cell synthesis as a sequential generation task. The resulting one-to-many generative model produces diverse unit cell geometries conditioned on target scattering responses. It achieves CST-validated designs with field transmission magnitude 0.9 across the full 0-$2\pi$ phase range at 20 GHz. Moreover, a CST-calibrated surrogate trained to accurately predict frequency responses (18-22 GHz) enables functional post-selection of inverse generated designs. Together, the hybrid SA-generative methodology in this two-part compilation establishes a scalable and data-efficient solution for multilayer HMS synthesis, with natural extensions toward broadband, oblique-incidence, and higher-dimensional electromagnetic inverse-design problems.

Modeling inviscid compressible flows with shocks and vortex dominated dynamics remains challenging for particle grid methods due to moving discontinuities, cell crossing noise, and quadrature degradation under strong deformation. Building on a FLIP/APIC framework with vorticity aware tensor artificial viscosity, we identify a long time RTI failure mode: particle depletion at spike heads degrades quadrature and particle grid coupling, producing nonphysical, void-like dents. Standard mitigations (CPDI lite and subcell-jittered seeding) reduce but do not eliminate this artifact. We therefore add two sampling-aware controls: (i) conservative split resampling that replenishes depleted cells while exactly conserving mass, momentum, and internal energy; and (ii) a soft-switch that attenuates only the APIC affine term when local support is insufficient. Tests on the Sod shock tube and single/multi mode RTI show that the method removes spike head voids in long-time RTI while preserving vortex roll up, and matches reference Euler growth metrics

Radiative near-field (RNF) beamforming is vulnerable to Fresnel-zone blockages. This letter proposes an obstruction-unaware wavefront shaping strategy inspired by catastrophe optics: a calibrated quartic phase generates a Pearcey-inspired wavepacket that improves effective multi-user channel separability in the tested partially blocked geometries. The quartic beam is calibrated in free space without obstruction information, while digital zero-forcing (ZF) uses the resulting effective channel to evaluate common SINR under a shared total transmit-power constraint. Numerical results demonstrate a peak sampled common-SINR gain of $4.29$~dB over conventional focusing when closely spaced users experience strong central blockage.

Femtosecond pulsed laser systems constitute powerful tools for the high-precision structuring of materials at micro/nano-scale resolutions. A critical parameter influencing the efficacy of ultrafast laser-material interactions is the laser-induced damage threshold (LIDT), which is defined as the minimum laser fluence required to induce irreversible modification to the material surface. While extensive studies have addressed single-pulse damage mechanisms, the response of thin metallic films to double-pulse femtosecond irradiation, particularly when the film thickness is of the order of the optical penetration depth, remains, generally, unexplored. In this work, we present a rigorous theoretical investigation into the spatiotemporal evolution of energy deposition, thermalization processes and optical parameter changes under double-pulse excitation conditions. The analysis considers key parameters including the inter-pulse delay and the film thickness to evaluate their influence on the LIDT for a range of technologically relevant metals: Au, Ag, Cu, Al, Ni, Ti, Cr, Pt, W, Mo and Stainless Steel (100Cr6). ). A comparative analysis highlights the potential of controlled double-pulse irradiation schemes to manipulate energy coupling efficiency, improve the spatial selectivity of laser-induced modifications and compile a comprehensive LIDT database for commonly used industrial materials. The approach is aimed to provide a robust foundation for the design and optimization of advanced laser micromachining and nanofabrication protocols across a broad spectrum of metallic systems.

Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain high-resolution projections for regions of interest, it is common to use Regional Climate Models (RCMs), which are driven by data produced by ESMs as boundary conditions. While more efficient than running ESMs at fine resolution, RCMs remain expensive and restrict the size of ensemble simulations. Inspired by recent advances in probabilistic machine learning for weather and climate, we introduce a data-driven climate downscaling method based on stochastic interpolants. Our approach efficiently transforms coarse ESM output into high-resolution regional climate projections at a fraction of the computational cost of traditional RCMs. Through extensive validation, we demonstrate that our method generates accurate regional ensembles, enabling both improved uncertainty quantification and broader use of high-resolution climate information.

Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metallic sheets, whose design often requires time-consuming full-wave optimization. Here, we extend analytical models originally developed for sparse loaded-wire metagratings to accurately describe densely packed Jerusalem-cross meta-atoms embedded in standard printed circuit board (PCB) dielectric stacks. The model captures both near- and far-field coupling within and between layers, enabling efficient prediction of the dual-polarized response. Using this framework, we identify highly transmissive meta-atoms whose phase is controlled by the leg lengths of the Jerusalem crosses (microscopic design stage). This (phase)-(leg-length) "lookup table" allows rapid synthesis of Huygens' metasurfaces (macroscopic design stage), demonstrated through a full-wave-validated metalens exhibiting low-reflection beam manipulation. Notably, we implement a judicious scaling method to further extend the model to predict wideband meta-atom responses. In the companion paper (Part II), a hybrid machine-learning approach leverages this semianalytical framework to enhance accuracy without requiring the conventional exhaustive full-wave training, enabling ultrafast inverse design across the full parameter space. Overall, the presented methodology – the standalone semianlytical scheme (Part I) and the machine-learning enhanced version (Part II) – establishes an effective open-source toolkit for versatile, rapid, and highly accurate synthesis of fabrication-ready dual-polarized transmissive Huygens' meta-atoms and metasurfaces.

Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive computational approaches for reconstructing three-dimensional activation dynamics. We present a physics-informed neural network framework for recovering cardiac activation patterns, active tension propagation, deformation fields, and hydrostatic pressure from measurable deformation data in simplified left ventricular geometries. Our approach integrates nonlinear anisotropic constitutive modeling, heterogeneous fiber orientation, weak formulations of the governing mechanics, and finite-element-based loss functions to embed physical constraints directly into training. We demonstrate that the proposed framework accurately reconstructs spatiotemporal activation dynamics under varying levels of measurement noise and reduced spatial resolution, while preserving global propagation patterns and activation timing. By coupling mechanistic modeling with data-driven inference, this method establishes a pathway toward patient-specific, non-invasive reconstruction of cardiac activation, with potential applications in digital phenotyping and computational support for arrhythmia assessment.

We ask whether the Hamiltonian, interaction geometry, and symmetries of a quantum many-body system can be inferred from a few low-lying eigenstates without knowing which sites interact with each other. Directly solving the eigenvalue equations imposes constraints that yield a highly degenerate subspace of candidate operators, where the local Hamiltonian is hidden among an extensive family of conserved quantities, obscuring the interaction geometry. Here we introduce O-Sensing, a protocol designed to extract the Hamiltonian and symmetries directly from these states. Specifically, O-Sensing employs parsimony-driven optimization to extract a maximally sparse operator basis from the degenerate subspace. The Hamiltonian is then selected from this basis by maximizing spectral entropy (effectively minimizing degeneracy) within the sampled subspace. We validate O-Sensing on Heisenberg models on connected Erdős–Rényi graphs, where it reconstructs the interaction geometry and uncovers additional long-range conserved operators. We establish a learnability phase diagram across graph densities, featuring a pronounced “confusion” regime where parsimony favors a dual description on the complement graph. These results show that sparsity optimization can reconstruct interaction geometry as an emergent output, enabling simultaneous recovery of the Hamiltonian and its symmetries from low-energy eigenstates.

This study investigates the influence of a perturbed eddy-viscosity model on linear stability and shape sensitivity of the global vortex rope mode arising in a hydro-turbine flow under fully turbulent conditions. The framework is based on the Reynolds-averaged Navier–Stokes equations with a standard $k$-$\varepsilon$ turbulence closure, linearized around a base-flow state. This base state is tuned to match the vortex-rope bifurcation predicted from three-dimensional unsteady simulations. The shape sensitivity of the global mode is derived, accounting for perturbations of both the base flow and the linear operator. We show that although the perturbed eddy-viscosity model has only a marginal effect on the eigenvalues and eigenmodes of interest, it substantially alters the resulting shape sensitivities. These differences arise primarily through the base-flow contribution to the total sensitivity, which dominates the sensitivity to shape deformations. Although both models identify coherent-velocity production and advection as the leading contributors, the linearized model captures additional mechanisms associated with eddy-viscosity perturbations. Comparison with experiments shows that only the perturbed eddy-viscosity model reproduces the correct trends in shape sensitivity, whereas the frozen model fails to do so. These findings highlight the importance of consistently linearizing turbulence models for sensitivity-based control of turbulent global instabilities.

Label-free vibrational microscopy provides chemically specific access to cellular structure, yet quantitative volumetric chemical dynamics in living cells remain largely inaccessible, particularly on subsecond timescales relevant to intracellular transport and structural reorganization. This limitation arises because most high-speed vibrational techniques rely on raster scanning, which constrains volumetric throughput to approximately one volume per second (vps). Although mid-infrared photothermal (MIP) imaging offers a pathway toward spatially parallel chemical detection, existing implementations have remained far below video-rate volumetric operation, reflecting a fundamental trade-off between imaging speed and signal-to-noise ratio. Here, we overcome this trade-off in MIP tomography and realize video-rate volumetric chemical imaging using mid-infrared photothermal optical diffraction tomography (MIP-ODT), achieving high photothermal sensitivity while maintaining quantitative measurement fidelity. High per-angle detectability supports volumetric reconstruction without temporal averaging, yielding a signal-to-noise ratio exceeding 70 under video-rate acquisition conditions. Consequently, volumetric imaging at 19.2 vps is achieved, representing a nearly 400-fold improvement over prior implementations. Using this capability, we performed video-rate three-dimensional tracking of lipid droplets in living cells and quantified anomalous diffusion from full volumetric trajectories, revealing heterogeneous intracellular transport behaviors that are obscured in two-dimensional measurements. We further demonstrate high-speed hyperspectral volumetric chemical imaging across a 300 cm-1 spectral window within 1 s through rapid MIR wavenumber sweeping, paving the way for real-time three-dimensional organelle-specific chemical phenotyping.

We report a phase-sensitive nonlinear x-ray response in the charge-density-wave material 1T-TaS2, revealed through x-ray parametric down-conversion into the ultraviolet. Extending nonlinear x-ray wave mixing beyond conventional crystalline systems to a correlated quantum material, we employ reciprocal-lattice phase matching to isolate distinct Fourier components of the nonlinear susceptibility. By selecting a fundamental reciprocal-lattice vector and a stacking-sensitive half-integer reciprocal-lattice vector, we probe the response across the nearly commensurate and incommensurate charge-density-wave phases. Tuning the ultraviolet photon energy through Ta O-shell resonances uncovers pronounced Fourier-component and phase-dependent resonant structure, indicating that the stacking-related nonlinear susceptibility couples differently to Ta-centered resonant states than the average lattice response. Remarkably, the nonlinear signal is strongly enhanced in the nearly commensurate phase despite weaker Bragg diffraction, demonstrating that the nonlinear susceptibility provides information inaccessible to linear probes. These results establish nonlinear x-ray spectroscopy as a phase-sensitive and orbital-selective probe of electronic reconstruction in quantum materials.

A simple numerical method for loading of a relativistic Maxwellian-type distribution is proposed based on inverse transform sampling. The relativistic Maxwellian energy distribution is introduced as an alternative to the Maxwell-Jüttner distribution. The cumulative distribution of the shifted-Maxwellian energy distribution is approximated by an invertible function. Random variates of energy is transformed from uniformly distributed random variables. Then, the energy variates are converted to momentum vector variates. Numerical tests are presented to show that the present method successfully reproduce the relativistic Maxwellian energy distribution.

For window applications in hot climates, it is desirable to have windows with high visible transparency, while maintaining strong reflectance in both the ultraviolet and near infrared, to minimize unwanted heat gain. Given that more than 70% of incident solar energy is at wavelengths shorter than 1000 nm, achieving spectrally abrupt transitions from transparent to reflecting at the boundaries of the visible is essential. Such abrupt transitions at multiple wavelengths typically would require tens of dielectric layers, which is impractical for most window applications. Here, we propose and realize a structure comprising only eight planar layers that achieves sharp reflectance changes at ~390 and ~680 nm, resulting in high visible transmittance (>70%), and high near-infrared (>80%) and UV (>60%) reflectance, as well as high mid-infrared emissivity (>90%) for additional radiative cooling. We demonstrate that our engineered window reduces air temperature by up to 3.8 {\deg}C inside an enclosed box simulating a vehicle, compared to a box with a reference window.

The inverse design of nonlocal metasurfaces requires the precise optimization of lattice geometry to engineer spatial dispersion and high-Q resonances. However, gradient-based optimization is frequently bottle-necked by the evaluation of the periodic Dyadic Green's Function (DGF), where traditional Finite Difference (FD) methods suffer from an inherent trade-off between truncation error and numerical instability near spectral singularities. In this work, we present an end-to-end Analytic Gradient Engine for 2D Bravais lattices. By mapping the spectral lattice sums of the Coupled Dipole Approximation (CDA) to the theory of Quasi-Modular Forms (QMF), we derive exact, closed-form expressions for the gradients of the interaction matrix with respect to the modular lattice parameter $\tau$. Our framework explicitly handles conditionally convergent terms via regularization and addresses the non-holomorphic outlier $\sigma_4^{(2)}$ via a hybrid numerical strategy. We further introduce a robust evaluation scheme combining $SL(2, \mathbb{Z})$ domain reduction with automatic error certificates. Experimental validation demonstrates that our engine achieves machine-precision derivatives ($10^{-15}$) and a 6.5$\times$ speedup in optimization convergence compared to finite-difference baselines, enabling the robust design of giant anisotropy in regimes where traditional methods fail.

The present study investigates influence of compressibility on separation induced transition in a low pressure turbine cascade using high fidelity direct numerical simulations of the T106A blade. Simulations are performed for inlet Mach numbers, Ms ranging from 0.15 to 0.35 at a fixed Reynolds number and high incidence, representative of off design LPT operation. A dispersion relation preserving numerical framework is employed to accurately capture instability waves, separation bubbles, and separation induced transition to turbulence. A comprehensive analysis is carried out using surface pressure and skin friction distributions, boundary layer integral parameters, spectral analyses, and budgets of compressible enstrophy. Increasing Ms systematically reduces streamwise extent of both leading edge and trailing edge separation bubbles and promotes earlier transition and reattachment, consistent with trends observed under increased free stream disturbances. Despite shorter separation regions, suction side momentum thickness at trailing edge increases from Ms = 0.15 to 0.35, indicating higher profile losses at elevated Ms. Spectral analyses demonstrate a redistribution of turbulent spatial and temporal scales, with energy injection occurring at progressively larger scales as Ms increases. Flow field visualizations reveal a transition pathway that shifts from two dimensional spanwise rolls and intermittent turbulent spots at low Ms to streak dominated, bypass like transition at higher Ms.

Parylene C thin films have significant applications in advanced packaging of microelectronics. Their thermal properties are critical for thermal management of electronic devices. However, a unified understanding of the tunable structure and the corresponding thermal conductivity is still missing. This study investigated parylene C thin films of varying thickness and post-annealing temperatures grown via thermal chemical vapor deposition. The ultralow thermal conductivity of as-deposited parylene C measured by time domain thermoreflectance (TDTR) is 0.10 W/m-K. The thermal conductivity can be tuned by post-annealing. Significant increase in thermal conductivity is observed in the annealed samples (0.18 W/m-K) which induces melting and recrystallization. The results of XRD and polarized Raman spectroscopy show that the enhanced thermal conductivity is due to improved crystalline quality and the change in chain orientations. The measured thermal conductivities of the as-deposited and annealed films are much lower than the values predicted by the Cahill minimum thermal conductivity model, which can be explained by the diffuson-mediated minimum thermal conductivity model. Parylene C is found to possess the lowest thermal conductivity among dense low-k materials. Our work provides guidance for the structural design of ultra-low thermal conductivity polymers and corresponding thermal design of electronics.

Coherent coupling between microwave cavity photons and magnon excitations enables quantum transduction, magnon-mediated entanglement, and magnon number-resolved detection. Micromagnetic simulation of photon-magnon coupling typically requires either modifying the core solver or implementing a full electromagnetic solver. Here we present a two-tier cavity magnonics extension for mumax+, a GPU-accelerated open-source micromagnetic framework. The first tier consists of CUDA kernels that integrate N cavity-mode ODEs simultaneously with the LLG equation inside the GPU-based RK45 adaptive time-stepper, eliminating per-step GPU-CPU transfers; spatially resolved mode profiles enter both the coupling and the feedback, enabling selective addressing of non-uniform spin-wave modes. The second tier is a lightweight Python co-simulation class that reproduces the same uniform-mode physics through operator-split RK4 integration without recompilation. We validate the implementation with eight benchmark simulations: (i) magnon-polariton anticrossing spectra, (ii) vacuum Rabi oscillations, (iii) the cooperativity phase diagram spanning weak-to-strong coupling regimes, (iv) cavity mode-profile-dependent coupling selection rules, (v) multi-mode polariton hybridization with magnon-mediated cavity-cavity energy transfer, (vi) mode-selective coupling via spatial overlap engineering, (vii) antiferromagnetic magnon-cavity coupling with Neel-vector spectroscopy, and (viii) abnormal anticrossing from dissipative photon-magnon coupling, demonstrating the transition from level repulsion to level attraction.

We propose to implement an optical analogue of the tautochrone property of the cycloid to allow the focusing of ultrashort pulses inside photonic systems. This allows to enhance nonlinear effects, resulting in orders of magnitude increase of nonlinearity-induced phase shifts, while employing low irradiances. Building upon the optical-mechanical analogy, we show how to produce optical limiters for temporal light pulses, and how to implement temporal bistability and even multistability with large numbers of states. Finally, we move this concept to the quantum realm and predict a tautochrone quantum blockade regime with a stronger antibunching.

All-sky OH airglow imaging provides two-dimensional observations of mesospheric wave morphology near the nominal OH emission peak altitude. Small, localized ripple-like structures are difficult to catalogue consistently through manual inspection and may represent either instability-related features or secondary gravity waves. We present an automated detection framework based on a squeeze-and-excitation convolutional neural network (SE-CNN) trained on 41 x 41-pixel patches extracted from 512 x 512-pixel all-sky images acquired at Yucca Ridge Field Station. Signed differences between consecutive images separated by approximately 2 min are normalized using spatial median-absolute-deviation scaling before classification. The trained model is applied to complete images through sliding-window inference, followed by score-map reconstruction, component filtering, and spatial matching. On the held-out patch test set, the classifier achieved an accuracy of 0.92, precision of 0.89, recall of 0.93, and F1 score of 0.92. Within a selected cohort of manually positive validation frames, 299 of 415 evaluable frames contained at least one reference-automated spatial overlap, and 324 of 559 eligible reference supports received a one-to-one automated-component assignment. In a separate blinded single-reviewer audit of disagreement patches, 411 of 543 model-candidate/reference-unmarked crops were classified as clear ripple-like, corresponding to a clear-ripple fraction of 75.7% with a night-cluster bootstrap 95% interval of 70.4%-80.2%. Because the audit was restricted to selected disagreement cases from manually positive frames, this value is not an archive-wide precision estimate.

In non-axisymmetric plasmas, the ambipolar condition may have multiple roots. In such cases, the evolution of the ambipolar electric field can be described by the dynamics in a bistable potential, where the relative depth of the potential wells primarily determines the realized root. In this study, we show that the inclusion of the magnetic drift in the orbit model can significantly modify the potential landscape and affect root selection. This effect provides a possible explanation for discrepancies between simulation results obtained using different orbit models, as well as between simulations and experimental observations of ambipolar radial electric field profiles. Further, the analysis suggests that the ambipolar electric field may be more susceptible to fluctuations than previously expected, indicating the potential relevance of noise-induced state transitions.

We consider a laser cooling and trapping of alkaline-earth and similar atoms in a bichromatic field resonant to a closed optical transition $^1S_0 \to \, ^1P_1$ or $^1S_0 \to \, ^3P_1$. It is shown that new kinetic effects emerge compared to monochromatic fields, enabling the formation of a deep macroscopic trap capable of capturing and cooling neutral atoms to sub-Doppler temperatures. Such a purely optical macroscopic trap can serve as an alternative to the well-known magneto-optical trap and can be used in applications requiring minimization of the magnetic field in the cold atom cloud region. The obtained results are of interest for the new generation of quantum sensors and optical frequency standards.